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The T-CREST project is developing a time-predictable system that will simplify the safety argument with respect to maximum execution time while striving to increase the performance with multicore processors.
Task with maximum execution time will be chosen.
We chose to use all possible images (30 fps).We set a maximum execution time for the matching task.
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Table6 shows the average, minimum and maximum execution times of 20 executions for both 8K×8K and 16K×16K block matrix multiplications.
Our main objective is to arrive at task assignments that could achieve minimum execution time, maximum node utilisation and a well-balanced load across all the nodes involved in a grid.
In [11], authors propose task scheduling algorithm to achieve minimum execution time, maximum processor utilization and optimal load balancing across different processors by defining tree objective functions.
In this work, we have demonstrated how two of these objective functions did not guarantee minimum execution time and maximum processor utilization and this independently of the virtualized application to which the scheduled tasks belong.
Urg = 13Urg = 5 Urg = 8, Urg = 2 Case of DFG (a) task B before task CCase of DFG (a) task C before task B. When a task has several successors with different implementations, the urgency is the maximum of execution times of the successors.
Finally, the maximum increase in the execution time of ApproxMap 1/10% compared with that of ApproxMap 5/12% was equal to 0.45.
Figure 3 GNBP (8-Body): execution time at maximum frequency.
Figure 7 Adaptive quadrature: execution time at maximum frequency.
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